When \(k\)  is known, the time-order-kept hierarchical clustering algorithm is implemented with the weighting techniques proposed in (REF ) and (REF ). To differentiate the two weighting techniques, we denote (REF ) as `simple weighting' and (REF ) as `iterative weighting'. We compare the performance of our approaches with other nonparametric methods: E-Divisive by ([1]}), Kernel Multiple Change Point(KernelMCP) by ([2]}), and MultiRank by ([3]}). For the fairness of the comparison, all the procedures are conducted with the number of change point \(k\)  known. The results are reported in Section 6.1 and Section 6.2 for univariate and multivariate settings, respectively. When \(k\)  is unknown, since it is hard to quantify the false discovery rate, only stability detection is applied in Section 6.3.
